The last several years have seen remarkable progress in the discovery of genetic variation that significantly influences the genetic risk of developing autism spectrum disorder (ASD). However, these studies have focused on only a small fraction of the genetic variation that can contribute to ASD.

Previous studies from Michael Talkowski and others that have analyzed sequencing data from the Simons Simplex Collection (SSC) and the Autism Sequencing Consortium (ASC) have suggested that the ability to define the complex genetic underpinnings of ASD would require many tens of thousands of samples and critical methodological advances to uniformly analyze various types of genomics technologies, in addition to very large data sets in a cloud-based computing environment. Developing such an infrastructure, together with carefully tested statistical models, will enable researchers to interpret the relative contribution of all classes of genomic variation to ASD.

Therefore, Talkowski and colleagues formed a large consortium effort, the SSC-ASC Genomics Consortium (SSC-GC), to aggregate large-scale ASD data sets, to develop bioinformatics and statistical models to analyze these data and to partner with experts in functional biology capable of evaluating new discoveries.

The current study, together with a related project from the SSC-GC, plans to integrate SPARK data with other genome, exome and microarray ASD data sets to perform the largest global gene discovery effort to date in ASD research. From this resource, Talkowski’s team plans to investigate genomic variation across the size and frequency spectrum. At their conclusion, these studies will estimate the contribution of coding and noncoding regulatory variation in ASD and provide foundational tools and data sets for future studies by the community.